Welcome to FastKG-Course, an interactive course designed to make you proficient in Knowledge Graphs, Semantic Technologies, Ontology, and more. This project is affiliated with the Laboratory for Assured AI Applications Development in the Center for Research Computing at the University of Notre Dame.
- Interactive Jupyter Notebooks
- Hands-on Tutorials
- Use of fast.ai and kglab libraries
- (Optional) Fork this repository
- Open the project in GitHub Codespaces
- The devcontainer will automatically set up your environment
- Start the Jupyter server and open the introductory notebook
Before starting this course, it is highly recommended to complete the following Fast.ai courses:
- Practical Deep Learning for Coders
- Deep Learning for Coders with fastai & Pytorch
- Jupyter Notebook 101
Additional requirements:
- Basic knowledge of Python
- Familiarity with machine learning concepts
Navigate through the Jupyter notebooks in sequence for a curated educational path, or feel free to explore topics that interest you.
The dev container is fully configured with software and KG and machine learning libraries needed for this course.
- Charles F Vardeman II
This project is part of the Laboratory for Assured AI Applications Development in the Center for Research Computing at the University of Notre Dame. The kglab project for providing some integrated tools for working with Knowledge Graphs as well as an excellent tutorial. Content generation and assistance have been facilitated using OpenAI's GPT-4 language model. ChatGPT August 3, 2023 version
This project is dual-licensed under:
- MIT License for the software components. See
LICENSE-MITfor more details. - Creative Commons Attribution 4.0 International (CC BY 4.0) for the educational content. See
LICENSE-CC-BYfor more details. - The
kglabsubmodule from derwen.ai is licensed under the MIT License.
You are free to use the project under either of the licenses, depending on your needs.